1015 results about "Time–frequency analysis" patented technology

The invention provides a pedestrian inertial positioning system based on indoor magnetic field feature assistance. The system comprises a magnetic field and inertial data obtaining module, a magnetic field positioning module, a pedestrian dead reckoning module, a positioning fusion module and an output module, wherein the magnetic field and inertial data obtaining module is used for acquiring magnetic field, accelerated speed and angular velocity information; the magnetic field positioning module is used for building a magnetic field feature library and carrying out time-frequency analysis on the magnetic field vector sequence in real time to extract the time-frequency feature, and matching with the magnetic field feature library to carry out magnetic field feature positioning; the pedestrian dead reckoning module is used for updating accelerated speed and angular velocity zero offset according to the condition that the step velocity discontinuity is zero during walking, judging the step number and calculating the step length and the direction of each step; the positioning fusion module is used for fusing a magnetic field feature positioning result and a pedestrian dead reckoning inertial positioning result by means of particle filter; and the output module is used for displaying a positioning result on web pages and terminals. The system provided by the invention has the characteristics of being independent from beacon during positioning, low in cost and consumption of positioning terminals, accurate in positioning result and adaptive to environment change.

By applying time-frequency analysis to a given standard iterative learning control or ILC an adaptive filter for the learned feed-forward loop is designed. This time varying filter varies according to the momentary frequency content of the error signal and allows to discriminate between areas of deterministic and stochastic error. Its application results in selective application of ILC to those intervals where error signals of high level are concentrated and allows application of a single ILC acquisition to different setpoint trajectories. The adaptive filter finds particular use in lithographic scanning systems where it is used for varying scan length.

The invention relates to a wind turbine generator bearing fault diagnosis method for a multi-channel deep convolutional neural network. The method comprises the steps of: simultaneously acquiring high-frequency vibration acceleration signals of a drive end and a non-drive end of a test bearing in various states by using a vibration acceleration sensor; analyzing the acquired vibration signals by using a time frequency analysis technology to obtain corresponding time frequency spectra; establishing a deep convolutional neural network diagnosis model, and training the diagnosis model by using the time frequency spectra and the states of the bearing as a training sample; evaluating the diagnosis model, and applying the diagnosis model to a bearing to be monitored. The method can realize automatic feature learning, avoids feature engineering, effectively utilizes multi-channel vibration signals, and has good universality and extensibility.

A forwarding looking ground penetrating mine detection apparatus includes a radiation source for irradiating a sample of ground suspected of containing at least one mine with a plurality of frequency swept ground penetrating radar signals. A detector receives target signals backscattered from the ground responsive to the radar signal. The detector includes a time-frequency analyzer which transforms the target signals into a time-frequency image representation (TFR). In a preferred embodiment, the detector can include a wavelet packet transformer (WPT) for extracting time-frequency localized information from the TFR in the form of feature set constructed from a wavelet table. The apparatus can also include a data dimensionality reducer for selecting features to form a feature subset from the feature set, preferably based on reference to a training data set. A multilayer neural network classifier can be based on the feature subset, and be adaptable to the surrounding environment through learning.

The vehicle classification method includes: the time-domain radar signal of single-frequency continuous wave radar is used as input to conduct time-frequency analysis to obtain a radar echo Doppler frequency spectrum chart changing along time; by Hafford counterchange the Doppler chart is mapped into a scattered central position parameter image; through characteristic picking-up, Karhunen-Loeve(K-L) screening and compressing, a characteristic sample is obtained, which is classified using Fisher rule to obtain the vehicle-type classification. For multi-type condition, multiple two-type classifiers are used to conduct respectively pairwise classification, according to the result of the classifiers said above the classification result is obtained by voting. Advantage: simple and reliable structure and low cost, conducting classification synchronously with detecting and speed-measuring of vehicles.

First image data and second image data are generated by capturing a subject image at a CCD. The first image data captured over a first exposure time manifest a relatively small extent of image blur. The second image data are captured over a second exposure time set longer than the first exposure time. A spatial frequency analysis is implemented on the first image data and the second image data to calculate the amplitude ratio and the phase difference of the high-frequency components in the two sets of image data. Based upon the amplitude ratio and the phase difference of the high-frequency components thus calculated, the spatial frequency component of the second image data is corrected to generate third image data in which any image blur has been corrected.

The invention discloses a time sequence analysis-based storm surge disaster early warning system, which comprises a database server, an early warning prediction model analysis module, an assistant decision making system, a Web server and a management user, wherein the database server stores historical monitoring data, real-time monitoring data, basic geographic information data, emergency plan data, related calculation model data and early warning data; the monitoring data and the early warning data are time sequence data; the early warning prediction model analysis module is mainly used for preprocessing the data, piecewise linearly expressing the time sequence data to fulfill dimension reduction and compression aims, performing similarity matching on the time sequence data and the historical monitoring data, and performing disaster early warning according to a similarity matching result; and the assistant decision making system displays assistant decision making information on the basis of geographical graphic information, and displays the assistant decision making information to a client of the management user through the deployed Web server.

The present invention provides a cochlear stimulation system and method for capturing and translating fine time structure (“FTS”) in incoming sounds and delivering this information spatially to the cochlea. The system comprises a FTS estimator/analyzer and a current navigator. An embodiment of the method comprises analyzing the incoming sounds within a time frequency band, extracting the slowly varying frequency components and estimating the FTS to obtain a more precise dominant FTS component within a frequency band. After adding the fine structure to the carrier to identify a precise dominant FTS component in each analysis frequency band (or stimulation channel), a stimulation current may be “steered” or directed, using the concept of virtual electrodes, to the precise spatial location (place) on the cochlea that corresponds to the dominant FTS component. This process is simultaneously repeated for each stimulation channel and each FTS component.

The invention discloses an online monitoring system and a method for partial discharge of an intelligent switch cabinet based on ultra-high frequency detection. The online monitoring system comprises a sensor unit, a signal conditioning unit, a MCU (Micro Control Unit) unit, a communication unit, a system power supply and an upper machine monitoring unit. The online monitoring method comprises the following steps that: the sensor unit finishes collection of partial discharge signals in the switch cabinet; the signal conditioning unit realizes amplification, filtering, frequency reduction and bias of the signals, thus obtaining signals which can be converted by an ADC (Analog to Digital Converter) module in a DSP (Digital Signal Processor); the MCU unit realizes analysis and procession of the signals; and data are uploaded to an upper machine in a monitoring room through an Ethernet. According to the online monitoring system and the method for the partial discharge of the intelligent switch cabinet based on the ultra-high frequency detection disclosed by the invention, collection and procession of high-frequency signals conducted by the DSP are realized by adopting a frequency spectrum shifting technology; a time-frequency analysis technology and a mode identification theory are applied to the monitoring system, therefore, the monitoring system is functional in fault diagnosis; and in addition, an IEC61850 standard is adopted by the system, consequently, management of various IEDs (Intelligent Electronic Devices) and interconnecting as well as networking of equipment in a transformer substation are convenient.

The spatial frequency analysis contained in the compressed image is used not only for decompression of images but also for the selection of an appropriate gamut mapping algorithm. Computation resources are then saved because no specific spatial frequency analysis is required for the selection of an appropriate gamut mapping algorithm.

An inversion-based algorithm for computing the time frequency analysis of reflection seismograms using constrained least-squares spectral analysis is formulated and applied to modeled seismic waveforms and real seismic data. The Fourier series coefficients are computed as a function of time directly by inverting a basis of truncated sinusoidal kernels for a moving time window. Spectra may be provided that have reduced window smearing for a given window length relative to the discrete Fourier transform irrespective of window shape, and a time-frequency analysis with a combination of time and frequency resolution that is superior to the short time Fourier transform and the continuous wavelet transform. The reduction in spectral smoothing enables enhanced determination of spectral characteristics of interfering reflections within a short window. The degree of resolution improvement relative to the short time Fourier transform increases as window length decreases.

The invention discloses a system for pre-testing and diagnosing the electro magnetic interference of electronic equipment, which is characterized by comprising a data collecting module, an interference source diagnosing and locating module, an environmental noise eliminating module and a visual time-frequency analyzer, wherein the data collecting module, the interference source diagnosing and locating module and the environmental noise eliminating module are respectively connected with the visual time-frequency analyzer, and the visual time-frequency analyzer achieves the drive of the data collecting module, the call of the interference source diagnosing and locating module and the environmental noise eliminating module and the monitoring and displaying of time-domain waveforms and frequency spectrums. The invention also discloses a method for pre-testing and diagnosing the electro magnetic interference of electronic equipment, which utilizes a LabView visual instrument platform and Matlab programming environmental developing software to pre-test and diagnose signals. The invention can pre-test and diagnose the electro magnetic interference of electronic equipment at common test fields, and has high automation and carrying degree and simple usage and reduces cost.

The invention discloses a portable partial discharge detecting and diagnosing device. The device comprises a partial discharge detector, a cable, a pre-sensor and a charger device, wherein the partial discharge detector comprises a lower computer and an upper computer; the lower computer comprises a simulation unit module, an FPGA module, an ARM module, a clock and reset module and a power management module; the upper computer is a Linux industrial personal computer. The portable partial discharge detecting and diagnosing device is provided with a 14-bit 100MHz two-channel acquisition system having the maximum storage depth of 64Mbit; the display of the characteristic spectrum of defects and the model identification of faults can be completed accurately in such a manner of PRPD clustering analysis in combination with impulse waveform time-frequency analysis; meanwhile, a radio-frequency sensor and an ultrahigh-frequency sensor are simultaneously provided, and the pulse current detection and the ultrahigh-frequency detection on primary equipment can be completed by replacing the pre-filter; the device is small in volume, light in weight, and capable of supplying power to a battery and convenient for field live testing and polling of a transformer.

The application relates to an ion trap mass spectrometer. An apparatus 41 and operation method are provided for an electrostatic trap mass spectrometer with measuring frequency of multiple isochronous ionic oscillations. For improving throughput and space charge capacity, the trap is substantially extended in one Z-direction forming a reproduced two-dimensional field. Multiple geometries are provided for trap Z-extension. The throughput of the analysis is improved by multiplexing electrostatic traps. The frequency analysis is accelerated by the shortening of ion packets and either by Wavelet-fit analysis of the image current signal or by using a time- of-flight detector for sampling a small portion of ions per oscillation. Multiple pulsed converters are suggested for optimal ion injection into electrostatic traps.

The invention provides an adaptive radiation source modulation identification method based on time-frequency analysis. The method comprises the steps of I, carrying out time-frequency analysis on a received radiation source signal by use of time frequency distribution, converting the radiation signal from a time-domain signal to a time-frequency two-dimensional image; II, reducing computation complexity and characteristic dimension by use of an image processing technology, and improving the proportion of signal characteristic information in the image through normalization, binaryzation, image thinning and image preprocessing operations; III, carrying out image shape characteristic extraction on the preprocessed image in combination with a second-order and four-order moment estimation method by use of an adaptive principal component analysis algorithm; and IV, identifying a modulation mode of the radiation source signal by use of an LIBSVM (Library for Support Vector Machine) classifier. According to the adaptive radiation source modulation identification method based on time-frequency analysis, the characteristic missing of a low signal to noise ratio signal can be effectively avoided, and characteristic redundancy of a high low signal to noise ratio can be also avoided, and the modulation identification rate is not affected at the same time.

The invention relates to an online detection method for a torsion vibration signal of an automotive power transmission system, and belongs to the field of automobile noise and vibration control. In the method, torsion vibration test and analysis are performed on a power transmission system of a running front-engine rear-drive independent suspension type vehicle. A testing disc and a rotating speed sensor are arranged at the tested position of the power transmission system and are used for measuring rotating signals of the power transmission system, performing self-adaptive filtering on the signals, performing time-frequency analysis on the filtered signals, and calculating the torsion vibration frequency of the system. The method is used for performing torsion test and analysis on the acceleration running working conditions of an actual vehicle, particularly the running front-engine rear-drive independent suspension type vehicle without a torsion vibration test rack. The testing disc and the rotating speed sensor are arranged at the tested position of the power transmission system and are used for measuring the rotating signals of the power transmission system, calculating the instantaneous rotating speed through the peak value detection, filtering, and calculating the torsion vibration frequency of the system through the time-frequency analysis of the filtered signals.

A method of real-time estimation of indicators of the engine combustion state from signals containing oscillating components. A signal, such as a vibration signal for example, correlated with the engine combustion, is acquired as a function of time or of the crank angle. Real-time filtering and time-frequency analysis of this signal is carried out and indicators of the combustion state are estimated in real time from this analysis. The method has application for combustion control of an internal-combustion engine.

The invention provides a micro moving target feature extracting method based on micro Doppler effect, which comprises the steps that HHT (Hilbert-Huang transform) is introduced into micro moving target feature extracting, an HHT algorithm based on the down sampling EMD (empirical mode decomposition) is provided by aiming at the problem of the modal mixing of the feature extracting of the HHT, and procedures of resolving, summing and averaging on the noisy EMD are performed by the multiple groups of data obtained by performing the down sampling on original signals, thus effectively solving the mixing problem of spectrogram modes in the vibration target feature extracting of the HHT, inhibiting the noise of the original signals, improving the signal to noise ratio, reducing the EMD operating complexity of the multiple groups of data, greatly reducing the operating amount, improving the operating speed and achieving a better micro Doppler feature extracting effect. The micro Doppler feature extracting model based on the improved HHT is provided by integrating the advantages of the traditional time frequency analysis method and the improved HHT algorithm, a spectrogram peak value estimation method is added into the model, the resolution in the traditional time frequency spectrogram is improved to be used as an assisting method for the HHT feature extracting, and the requirements on accuracy and practicability of the extracted vibration target feature are achieved.

The embodiment of the invention discloses a non-contact heart rate measurement method and a device based on Euler image magnification, used for solving the technical problems existing in the existingnon-contact heart rate measurement method that the heart rate is great in same-frequency noise interference and is greatly influenced by an environment temperature, isolation signal authenticity lacksjudgment and the like. The non-contact heart rate measurement method and device based on Euler image magnification in the embodiment of the invention comprises the following steps: S1, magnifying tiny change of radial artery jump in a video through an Euler image magnification technology; S2, performing luminance variance statistics on a video frame pixel point luminance channel after magnifyingthe tiny change of radial artery jump in a time domain, performing skin segmentation on the video frame subjected to magnification of the tiny change of radial artery jump, and extracting a radial artery jump area; and S3, performing time-frequency analysis on the radial artery jump area so as to calculate the heart rate.